The power of internet protocol (IP) networks, such as the Internet, is their connectionless method of transporting data from source to destination. The nature of this connectionless transport is embodied in the “forward and forget” paradigm of the IP network's most powerful tool: the router. However, this greatest strength is also the IP network's greatest weakness. The “forward and forget” philosophy inherent in the network insures that there is no information about any data packet, and consequently any session, or flow, maintained by the network. Without information about packets or flows that the network could use to treat one packet or flow differently than the rest, the network must treat every packet and flow the same, resulting in the best efforts form of quality of service, as anyone who has ever used the Internet is familiar with.
To avoid the “forward and forget” paradigm, the network needs to be able to learn and maintain knowledge of the characteristics of the data packets and flows passing through it. Additionally, the network should learn and remember the events that are contained within the contents of those data packets or flows. With this knowledge, the network would have the information necessary to distinguish between packets and flows, and give those with particular characteristics or events treatment different from the other packets in the network. For example, if the network was able to recognize a streaming video flow, the network could assign a higher quality of service to that flow to ensure that it passed through the network in the most efficient fashion. Similarly, if the network could recognize data packets from a user who had paid a premium for better service, the network could ensure that those data packets received higher quality of service than packets from users who had not paid the premium. Further, if the network could recognize events within flows, such as an email infected with a virus, the network could act on that information and discard the email or strip the infected attachment.
Accordingly, what is needed is a network processing system that can act as a learning state machine. The learning state machine is able to examine data packets and flows and learn characteristics about and events in those data packets and flows, save those characteristics and events as state, and to modify and direct the data packets and flows based, at least in part, on the state information.